# Optimization of physical energy and velocity allocation for cyclists in road cycling individual time trial using genetic algorithm

## TL;DR

This paper introduces a genetic algorithm to optimize energy and speed for cyclists in time trials, especially on curves and slopes, improving performance by up to 9.7%.

## Contribution

A novel genetic algorithm-based strategy for optimizing cyclist energy and speed allocation on curves and slopes.

## Key findings

- Time was reduced by 9.7% on a 400-m track using the new strategy.
- The strategy also reduced time by 6.35% during bridge testing.
- Validation on the 2024 Paris Olympic course confirmed the strategy's effectiveness.

## Abstract

Effective energy management for optimizing energy and speed allocation for athletes in road cycling individual time trials is crucial due to the race’s long distances. Existing strategies often consume excessive body energy due to inadequately addressing the impact of slopes and curves.

We propose an advanced energy allocation strategy using a genetic algorithm. Our research focuses on optimizing speed and energy allocation specifically in curves and on slopes given factors such as air resistance, friction, gravity and weather to maximize athletes’ energy efficiency during time trials. For curve optimization, we optimize the athletes' cornering strategies based on the parameters including road width, inner curve radius and curve angles.

The simulation results demonstrate that time is reduced by 9.7% on a standard 400-m track and time is reduced by 6.35% on bridge testing comparing with pre optimization strategies.

We validate the optimizing strategy based on the 2024 Paris Olympic Games road cycling individual time trial course, which demonstrates the effectiveness of the strategy. This research provides athletes with valuable guidance for optimal energy distribution.